MCP server that converts file contents into compact, line-numbered PNG images for vision models to read, reducing token usage by roughly 7x for large files.
Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.
An MCP server that provides local image recognition on macOS, including OCR, image classification, comprehensive image analysis, and screenshot recognition, all via Apple's Vision framework without any network requests.
Enables integration between MCP clients and the Handwriting OCR service, allowing users to upload images and PDF documents, check processing status, and retrieve OCR results as Markdown.
Provides image recognition capabilities using Anthropic Claude Vision and OpenAI GPT-4 Vision APIs, supporting multiple image formats and offering optional text extraction via Tesseract OCR.
A Model Context Protocol server that gives AI assistants OCR with first-class accuracy handling and evaluation. It wraps three engines behind one interface and can score and compare them.
A lightweight server that provides detailed text analysis, counting total characters, characters without spaces, letters, numbers, and symbols for AI assistants like Claude Desktop and GitHub Copilot.
Extracts text from images using Tesseract OCR with support for local files, URLs, and raw image bytes. It provides production-grade OCR capabilities and multi-language support through the Model Context Protocol.
Provides detailed character analysis for text, counting total characters, characters without spaces, letters, numbers, and symbols through a simple MCP tool interface.
Generates random Universal LPC spritesheet characters as full animation PNGs, compositing real sprite layers via a Cloudflare Worker with browser rendering.